Search

The replacement of synthetic chemical herbicides and traditional plastic sheets is a major challenge of modern horticulture in view of a sustainable weed management. In the first step of this research, we tested the weed control efficacy of two biodegradable polymers, chitosan and galactomannan, applied to the soil surface as spray mulching, with or without the addition of charcoal as a light masking agent, and five essential oils with recognized herbicide properties. The results showed the ability of chitosan in reducing the number and the biomass of annual plants, regardless of the addition of charcoal and essential oils. In the second step, we tested the efficacy of one or three days of false seeding to increase the effectiveness of chitosan against seed germination. The results showed, on average, a reduction of 79% of annual weed presence after three days of false seeding. In both steps, the microbial biomass and three indicators of microbial activity (i.e., basal respiration, FDA hydrolysis activity, and D-glucosamine-induced respiration) were measured in the soil under the experiments in order to investigate possible alterations of soil biological activity induced by the treatments. The results provided no evidence of negative impact of the treatments on soil microbial biomass and activity. [ABSTRACT FROM AUTHOR]

Chemometric analysis applied to diffuse reflectance spectroscopy is increasingly proposed as an effective and accurate methodology to predict soil physical, chemical and biological properties. Its effectiveness, however, largely varies in relation to the calibration techniques and the specific soil properties. In addition, the calibration of UV-Vis-NIR spectra usually requires large datasets, and the identification of techniques suitable to deal with small sample sizes and high dimensionality problems is a primary challenge. In order to investigate the predictability of many soil chemical and biological properties from a small dataset and to identify the most suitable techniques to deal with this type of problems, we analysed 20 top soil samples of three different forests (Fagus sylvatica, Quercus cerris and Quercus ilex) in southern Apennines (Italy). Diffuse reflectance spectra were recorded in the UV-Vis-NIR range (200-2500 nm) and 22 chemical and biological properties were analysed. Three different calibration techniques were tested, namely the Partial Least Square Regression (PLSR), the combinations wavelet transformation/Elastic net and wavelet transformation/Supervised Principal Component (SPC) regression/ Least Absolute Shrinkage and Selection Operator (LASSO), a kind of preconditioned LASSO. Calibration techniques were applied to both raw spectra and spectra subjected to wavelet shrinkage filtering, in order to evaluate the influence on predictions of spectra denoising. Overall, SPC/LASSO outperformed the other techniques with both raw and denoised spectra. Elastic net produced heterogeneous results, but outperformed SPC/LASSO for total organic carbon, whereas PLSR produced the worst results. Spectra denoising improved the prediction accuracy of many parameters, but worsen the predictions in some cases. Our approach highlighted that: (i) SPC/LASSO (and Elastic net in the case of total organic carbon) is especially suitable to calibrate spectra in the case of small, high dimensional datasets; and (ii) spectra denoising could be an effective technique to improve calibration results. [ABSTRACT FROM AUTHOR]